For this challenge, the same housing dataset will be used. However, now it has two features: age and area of the house (columns `'age'` and `'square_feet'`).

import pandas as pd

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b22d1166-efda-45e8-979e-6c3ecfc566fc/houseprices.csv')
print(df.head())

import unittest
import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression


# Codefinity helper
def _dynamic_test(test_case, condition, success, fail):
    if condition:
        test_case._testMethodName = success
        test_case.assertTrue(True)
    else:
        test_case._testMethodName = fail
        test_case.fail(fail)


class TestUserCode(unittest.TestCase):

    # 1. Test that X contains the correct columns
    def test_X_contains_correct_columns(self):
        import user_code

        cond = False
        try:
            X = user_code.X
            cond = list(X.columns) == ["age", "square_feet"]
        except Exception:
            cond = False

        _dynamic_test(
            self,
            cond,
            "X correctly contains ['age', 'square_feet'].",
            "Expected X to contain columns: ['age', 'square_feet']."
        )

    # 2. Model is initialized correctly
    def test_model_is_linear_regression(self):
        import user_code
        
        cond = hasattr(user_code, "model") and isinstance(user_code.model, LinearRegression)

        _dynamic_test(
            self,
            cond,
            "model is an instance of LinearRegression.",
            "Expected model = LinearRegression()"
        )

    # 3. Model is fitted
    def test_model_is_fitted(self):
        import user_code
        
        cond = False
        try:
            # Check for learned attributes
            cond = hasattr(user_code.model, "coef_") and hasattr(user_code.model, "intercept_")
        except Exception:
            cond = False

        _dynamic_test(
            self,
            cond,
            "The model is fitted.",
            "Expected model.fit(X, y) to be called."
        )

    # 4. Test predictions
    def test_predictions(self):
        import user_code
        import numpy as np

        cond = False
        try:
            y_pred = user_code.y_pred
            cond = (
                isinstance(y_pred, np.ndarray)
                and y_pred.shape == (3,)
            )
        except Exception:
            cond = False

        _dynamic_test(
            self,
            cond,
            "y_pred is a NumPy array with correct shape (3,).",
            "Expected y_pred = model.predict(X_new) to have shape (3,)."
        )

    # 5. Check printed output logic (optional, but ensures they printed params)
    def test_params_printed(self):
        import user_code
        
        with open("user_code.py", "r") as f:
            src = f.read()
            
        cond = "model.intercept_" in src and "model.coef_" in src
        
        _dynamic_test(
            self,
            cond,
            "Model parameters are printed.",
            "Expected printing of `model.intercept_` and `model.coef_`."
        )


if __name__ == "__main__":
    unittest.main()

test_code.py

If you did everything right, you got the p-values close to zero. That means all our features are significant for the model.

Master the core algorithms of supervised learning and implement them using Scikit-learn. Explore linear and polynomial regression for price prediction, and transition into classification using k-NN, Logistic Regression, and Decision Trees. Learn to evaluate models through cross-validation, manage overfitting with regularization, and optimize hyperparameters. Build robust predictive systems and define complex decision boundaries for multi-class classification tasks.

Challenge: Predicting Prices Using Two Features

Solution

Challenge: Predicting Prices Using Two Features

Solution